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bqror: An R package for Bayesian Quantile Regression in Ordinal Models

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  • Prajual Maheshwari
  • Mohammad Arshad Rahman

Abstract

This article describes an R package bqror that estimates Bayesian quantile regression for ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers computationally efficient, yet simple, Markov chain Monte Carlo (MCMC) algorithms for estimating ordinal quantile regression. The generic ordinal model with 3 or more outcomes (labeled ORI model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled ORII model) is estimated using Gibbs sampling only. In line with the Bayesian literature, we suggest using marginal likelihood for comparing alternative quantile regression models and explain how to compute the same. The models and their estimation procedures are illustrated via multiple simulation studies and implemented in two applications. The article also describes several other functions contained within the bqror package, which are necessary for estimation, inference, and assessing model fit.

Suggested Citation

  • Prajual Maheshwari & Mohammad Arshad Rahman, 2021. "bqror: An R package for Bayesian Quantile Regression in Ordinal Models," Papers 2109.13606, arXiv.org, revised May 2023.
    • Handle: RePEc:arx:papers:2109.13606
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    Cited by:

    1. Ivan Jeliazkov & Shubham Karnawat & Mohammad Arshad Rahman & Angela Vossmeyer, 2023. "Flexible Bayesian Quantile Analysis of Residential Rental Rates," Papers 2305.13687, arXiv.org, revised Sep 2023.

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